ENAC Seminar Series by Dr K. Wang
Event details
| Date | 12.04.2021 |
| Hour | 14:00 › 15:00 |
| Speaker | Dr Kai Wang |
| Location | Online |
| Category | Conferences - Seminars |
14:00 – 15:00 – Dr K. Wang
Postdoctoral Associate at Massachusetts Institute of Technology, USA
Vertiport Planning for Urban Aerial Mobility: An Adaptive Discretization Approach
Mobility systems are experiencing unprecedented transformations due to rapid technological developments (e.g., vehicle technologies, information and communication technologies) and new business models (e.g., ride-sharing, on-demand business). At the same time, the mobility sector is facing increasing pressure to meet sustainability goals, ranging from mitigating climate change to creating equitable access. Addressing these challenges requires novel analytics and optimization methods to design a pathway toward human-centric mobility. This talk explores these broad themes, with a particular focus on Urban Aerial Mobility (UAM), enabled by the rise of electric vertical-takeoff-and-landing (eVTOL) vehicles.
The immediate decision faced by the future UAM operator involves determining the number, location, and capacity of vertiports in a metropolitan area. We formulate an optimization model that captures interdependencies between strategic vertiport deployment, tactical eVTOL operations, and customer adoption. The model includes a “tractable part” (a mixed-integer second-order conic program) but also a non-convex customer adoption function that makes it intractable—a common problem when capturing demand-supply interactions in mobility systems. We develop an original exact algorithm based on adaptive discretization, which dynamically refines “promising” regions while retaining computational tractability. Computational results suggest that the algorithm converges to a 1% optimality gap, dominating traditional static discretization in terms of solution quality, runtimes, and solution guarantee. From a practical standpoint, we find that UAM networks vary widely across metropolitan areas, as a function of geographic, urban, and commuting patterns. Vertiport networks grow in a nested fashion, starting with a few “obvious” vertiports and adding vertiports as penetration increases. Operationally, UAM supports primarily long trips, with complex dynamics to enable passenger pooling and vehicle recharging. We uncover two potential use cases for UAM technologies: airport shuttle and long-distance commutes.
Short bio:
Kai Wang is a Postdoctoral Associate from the Massachusetts Institute of Technology. He obtained his PhD degree from The Hong Kong Polytechnic University in 2019. He was also a visiting PhD student at Carnegie Mellon University. Kai Wang’s research spans large-scale, stochastic, and data-driven optimization, with applications in mobility and logistics systems. His research has tackled a wide range of real-world problems, spanning urban mobility, aviation, maritime transportation, and smart cities. His research has appeared in top-tier journals such as Operations Research, Transportation Science, and Transportation Research Part B. It has been recognized by several academic distinctions, e.g., the Best Paper Award in the Applied Track from the 15th INFORMS Workshop on Data Mining and Decision Analytics (2020).
Postdoctoral Associate at Massachusetts Institute of Technology, USA
Vertiport Planning for Urban Aerial Mobility: An Adaptive Discretization Approach
Mobility systems are experiencing unprecedented transformations due to rapid technological developments (e.g., vehicle technologies, information and communication technologies) and new business models (e.g., ride-sharing, on-demand business). At the same time, the mobility sector is facing increasing pressure to meet sustainability goals, ranging from mitigating climate change to creating equitable access. Addressing these challenges requires novel analytics and optimization methods to design a pathway toward human-centric mobility. This talk explores these broad themes, with a particular focus on Urban Aerial Mobility (UAM), enabled by the rise of electric vertical-takeoff-and-landing (eVTOL) vehicles.
The immediate decision faced by the future UAM operator involves determining the number, location, and capacity of vertiports in a metropolitan area. We formulate an optimization model that captures interdependencies between strategic vertiport deployment, tactical eVTOL operations, and customer adoption. The model includes a “tractable part” (a mixed-integer second-order conic program) but also a non-convex customer adoption function that makes it intractable—a common problem when capturing demand-supply interactions in mobility systems. We develop an original exact algorithm based on adaptive discretization, which dynamically refines “promising” regions while retaining computational tractability. Computational results suggest that the algorithm converges to a 1% optimality gap, dominating traditional static discretization in terms of solution quality, runtimes, and solution guarantee. From a practical standpoint, we find that UAM networks vary widely across metropolitan areas, as a function of geographic, urban, and commuting patterns. Vertiport networks grow in a nested fashion, starting with a few “obvious” vertiports and adding vertiports as penetration increases. Operationally, UAM supports primarily long trips, with complex dynamics to enable passenger pooling and vehicle recharging. We uncover two potential use cases for UAM technologies: airport shuttle and long-distance commutes.
Short bio:
Kai Wang is a Postdoctoral Associate from the Massachusetts Institute of Technology. He obtained his PhD degree from The Hong Kong Polytechnic University in 2019. He was also a visiting PhD student at Carnegie Mellon University. Kai Wang’s research spans large-scale, stochastic, and data-driven optimization, with applications in mobility and logistics systems. His research has tackled a wide range of real-world problems, spanning urban mobility, aviation, maritime transportation, and smart cities. His research has appeared in top-tier journals such as Operations Research, Transportation Science, and Transportation Research Part B. It has been recognized by several academic distinctions, e.g., the Best Paper Award in the Applied Track from the 15th INFORMS Workshop on Data Mining and Decision Analytics (2020).
Practical information
- General public
- Invitation required
- This event is internal
Organizer
- ENAC
Contact
- Cristina Perez